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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q60-Q65):

NEW QUESTION # 60
A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures. What would be the most effective strategy to enhance the system's real-time processing capabilities?

A. Implement edge computing to preprocess sensor data closer to the source before sending it to the central AI system
B. Increase the frequency of sensor data collection to provide more detailed inputs for the AI model
C. Use a more complex AI model to enhance prediction accuracy
D. Reduce the number of sensors to decrease the amount of data the AI system must process

Answer: A

Explanation:
Implementing edge computing to preprocess sensor data closer to the source is the most effective strategy to enhance real-time processing capabilities for a predictive maintenance system. Using NVIDIA Jetson devices at the edge, raw sensor data can be filtered, aggregated, or preprocessed (e.g., via DeepStream), reducing the volume sent to the central GPU cluster (e.g., DGX). This lowers latency and ensures timely predictions, as outlined in NVIDIA's "Edge AI Solutions" and "AI Infrastructure for Enterprise." Reducing sensors (A) risks missing critical data. A more complex model (B) increases processingdemands, worsening delays. Higher data frequency (D) exacerbates the bottleneck. Edge computing is NVIDIA's recommended solution for real-time IoT workloads.

NEW QUESTION # 61
Your company is planning to deploy a range of AI workloads, including training a large convolutional neural network (CNN) for image classification, running real-time video analytics, and performing batch processing of sensor data. What type of infrastructure should be prioritized to support these diverse AI workloads effectively?

A. CPU-only servers with high memory capacity
B. On-premise servers with large storage capacity
C. A cloud-based infrastructure with serverless computing options
D. A hybrid cloud infrastructure combining on-premise servers and cloud resources

Answer: D

Explanation:
Diverse AI workloads-training CNNs (compute-heavy), real-time video analytics (latency-sensitive), and batch sensor processing (data-intensive)-require flexible, scalable infrastructure. A hybrid cloud infrastructure, combining on-premise NVIDIA GPU servers (e.g., DGX) with cloud resources (e.g., DGX Cloud), provides the best of both: on-premise control for sensitive data or latency-critical tasks and cloud scalability for burst compute or storage needs. NVIDIA's hybrid solutions support this versatility across workload types.
On-premise alone (Option A) lacks scalability. CPU-only servers (Option B) can't handle GPU-accelerated AI efficiently. Serverless cloud (Option C) suits lightweight tasks, not heavy AI workloads. Hybrid cloud is NVIDIA's strategic fit for diverse AI.

NEW QUESTION # 62
In a complex AI-driven autonomous vehicle system, the computing infrastructure is composed of multiple GPUs, CPUs, and DPUs. During real-time object detection, which of the following best explains how these components interact to optimize performance?

A. The GPU processes the object detection model, the DPU offloads network traffic from the GPU, and the CPU is unused.
B. The GPU handles object detection algorithms, while the CPU manages the vehicle's control systems without DPU involvement.
C. The GPU processes object detection algorithms, the CPU handles decision-making logic, and the DPU offloads network and storage tasks.
D. The CPU processes the object detection model, while the GPU and DPU handle data preprocessing and network traffic.

Answer: C

Explanation:
In NVIDIA's autonomous vehicle platforms (e.g., DRIVE AGX), GPUs, CPUs, and DPUs (Data Processing Units like BlueField) work synergistically. GPUs excel at parallel processing for object detection algorithms (e.g., CNNs), delivering the high compute power needed for real-time performance. CPUs handle decision- making logic, such as path planning or control, leveraging their sequential processing strengths. DPUs offload network and storage tasks (e.g., sensor data ingestion), reducing the burden on GPUs and CPUs, enhancing overall system efficiency.
Option B is incorrect-CPUs lack the parallelization for efficient object detection. Option C underestimates the CPU's role, which is critical for decision-making. Option D ignores the DPU's contribution, which NVIDIA emphasizes for I/O optimization in DRIVE systems. Option A aligns with NVIDIA's documented architecture for autonomous driving.

NEW QUESTION # 63
Your team is building an AI-powered application that requires the deployment of multiple models, each trained using different frameworks (e.g., TensorFlow, PyTorch, and ONNX). You need a deployment solution that can efficiently serve all these models in production, regardless of the framework they were built in.
Which software component should you choose?

A. NVIDIA DeepOps
B. NVIDIA Triton Inference Server
C. NVIDIA Clara Deploy SDK
D. NVIDIA TensorRT

Answer: B

Explanation:
NVIDIA Triton Inference Server is the best choice for deploying multiple models from different frameworks (TensorFlow, PyTorch, ONNX) in production. Triton provides a unified platform for serving models, supporting diverse frameworks with high performance on NVIDIA GPUs via features like dynamic batching and multi-model management. Option A (Clara Deploy SDK) is healthcare-specific. Option B (TensorRT) optimizes inference but isn't a full serving solution. Option C (DeepOps) aids deployment automation, not model serving. NVIDIA's Triton documentation emphasizes its versatility and efficiency for production inference across frameworks.

NEW QUESTION # 64
Your company is implementing a hybrid cloud AI infrastructure that needs to support both on-premises and cloud-based AI workloads. The infrastructure must enable seamless integration, scalability, and efficient resource management across different environments. Which NVIDIA solution should be considered to best support this hybrid infrastructure?

A. NVIDIA Triton Inference Server
B. NVIDIA Clara Deploy SDK
C. NVIDIA MIG (Multi-Instance GPU)
D. NVIDIA Fleet Command

Answer: D

Explanation:
NVIDIA Fleet Command is the best solution for supporting a hybrid cloud AI infrastructure with seamless integration, scalability, and efficient resource management. Fleet Command is a cloud-based platform for managing and orchestrating NVIDIA GPU workloads across on-premises and cloud environments. It provides centralized control, deployment, and monitoring, ensuring consistency and scalability for AI tasks, as detailed in NVIDIA's "Fleet Command Documentation." MIG (A) optimizes single-GPU partitioning, not hybrid management. Triton (B) handles inference deployment, not full infrastructure orchestration. Clara Deploy SDK (C) is healthcare-specific. Fleet Command is NVIDIA's hybrid AI management solution.

NEW QUESTION # 65
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Pass-Sure NCA-AIIO Vce Files | Amazing Pass Rate For NCA-AIIO: NVIDIA-Certified Associate AI Infrastructure and Operations | Useful NCA-AIIO Test Simulator

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q60-Q65):

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